XSEDE ECSS, Blacklight and Stampede Support California Yellowtail Genome Assembly

April 12, 2018

April 12, 2018 — If you eat fish in the U.S., chances are it once swam in another country. That’s because the U.S. imports over 80 percent of its seafood, according to estimates by the United Nations. New genetic research could help make farmed fish more palatable and bring America’s wild fish species to dinner tables. Scientists have used big data and supercomputers to catch a fish genome, a first step in its sustainable aquaculture harvest.

Researchers assembled and annotated for the first time the genome – the total genetic material – of the fish species Seriola dorsalis. Also known as California Yellowtail, it’s a fish of high value to the sashimi, or raw seafood industry. The science team formed from the Southwest Fisheries Science Center of the U.S. National Marine Fisheries Service, Iowa State University, and the Instituto Politécnico Nacional in Mexico. They published their results on January of 2018 in the journal BMC Genomics.

“The major findings in this publication were to characterize the Seriola dorsalis genome and its annotation, along with getting a better understanding of sex determination of this fish species,” said study co-author Andrew Severin, a Scientist and Facility Manager at the Genome Informatics Facility of Iowa State University.

“We can now confidently say,” added Severin, “that Seriola dorsalis has a Z-W sex determination system, and that we know the chromosome that it’s contained on and the region that actually determines the sex of this fish.” Z-W refers to the sex chromosomes and depends on whether the male or female is heterozygous (XX,XY or ZZ,ZW), respectively. Another way to think about this is that in Z-W sex determination, the DNA molecules of the fish ovum determine the sex of the offspring. By contrast, in the X-Y sex determination system, such is found in humans, the sperm determines sex in the offspring.

It’s hard to tell the difference between a male and female yellowtail fish because they don’t have any obvious phenotypical, or outwardly physically distinguishing traits. “Being able to determine sex in fish is really important because we can develop a marker that can be used to determine sex in young fish that you can’t determine phenotypically,” Severin explained. “This can be used to improve aquaculture practices.” Sex identification lets fish farmers stock tanks with the right ratio of males to females and get better yield.

Assembling and annotating a genome is like building an enormous three-dimensional jigsaw puzzle. The Seriola dorsalis genome has 685 million pieces – its base pairs of DNA – to put together. “Gene annotations are locations on the genome that encode transcripts that are translated into proteins,” explained Severin. “Proteins are the molecular machinery that operate all the biochemistry in the body from the digestion of your food, to the activation of your immune system to the growth of your fingernails. Even that is an oversimplification of all the regulation.”

Severin and his team assembled the genome of 685 megabase (MB) pairs from thousands of smaller fragments that each gave information to form the complete picture. “We had to sequence them for quite a bit of depth in order to construct the full 685 MB genome,” said study co-author Arun Seetharam. “This amounted to a lot of data,” added Seetharam, who is an associate scientist at the Genome Informatics Facility of Iowa State University.

The raw DNA sequence data ran 500 gigabytes for the Seriola dorsalis genome, coming from tissue samples of a juvenile fish collected at the Hubbs SeaWorld Research Institute in San Diego. “In order to put them together,” Seetharam said, “we needed a computer with a lot more RAM to put it all into the computer’s memory and then put it together to construct the 685 MB genome. We needed really powerful machines.”

That’s when Seetharam realized that the computational resources at Iowa State University at the time weren’t sufficient get the job done in a timely manner, and he turned to XSEDE, the eXtreme Science and Engineering Discovery Environment funded by the National Science Foundation. XSEDE is a single virtual system that scientists can use to interactively share computing resources, data and expertise.

“When we first started using XSEDE resources,” explained Seetharam, “there was an option for us to select for ECSS, the Extended Collaborative Support Services. We thought it would be a great help if there were someone from the XSEDE side to help us. We opted for ECSS. Our interactions with Phillip Blood of the Pittsburgh Supercomputing Center were extremely important to get us up and running with the assembly quickly on XSEDE resources,” Seetharam said.

The genome assembly work was computed at the Pittsburgh Supercomputing Center (PSC) on the Blacklightsystem, which at one point was the world’s largest coherent shared-memory computing system. Blacklight has since been superseded by the data-centric Bridges system at PSC, which includes similar large-memory nodes of up to 12 terabytes — a thousand times more than a typical personal computer. “We ended up using Blacklight at the time because it had a lot of RAM,” recalled Andrew Severin. That’s because they needed to put all the raw data into the computer’s random access memory (RAM) so that it could use the algorithms of the Maryland Super-Read Celera Assembler genome assembly software. “You have to be able to compare every single piece of sequence data to every other piece to figure out which pieces need to be joined together, like a giant puzzle,” Severin explained.

“We also used Stampede,” continued Severin, “the first Stampede, which is another XSEDE computational resource that has lots and lots of compute nodes. Each compute node you can think of as a separate computer. ” The Stampede1 system at the Texas Advanced Computing Center had over 6,400 Dell PowerEdge server nodes, which later added 508 Intel Knights Landing (KNL) nodes in preparation for its current successor, Stampede2 with 4,200 KNL nodes.

“We used Stampede to do the annotation of these gene models that we identified in the genome to try and figure out what their functions are,” Severin said. “That required us to perform an analysis called the Basic Local Alignment Search Tool (BLAST), and it required us to use many CPUs, over a year’s worth of compute time that we ended up doing within a couple of week’s worth of actual time because of the many nodes that were on Stampede.”

To read the full article, click here.


Source: TACC

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